Skill UI

A comprehensive guide for constructing and auditing empirical data pipelines, specifically tailored for high-impact journals. It covers advanced causal inference techniques (DID, RDD, IV, RCT), detailed sample management, robustness testing, and creating structured comparative estimates to withstand intense academic scrutiny.

This router provides a comprehensive, step-by-step pipeline for authors writing for top-tier international economics journals (like JIE). It guides the user through the entire academic lifecycle—from initial topic selection and literature positioning, through identification strategy, data analysis, drafting, to final submission and rebuttal. It ensures the manuscript meets rigorous standards for scope, originality, and data integrity.

This guide walks authors through the entire academic publishing lifecycle for international marketing journals. It details the editorial pipeline, from initial administrative checks and editor screening to the detailed review process, including common desk-reject triggers (e.g., single-country data, lack of equivalence evidence). It also provides strategies for productive waiting, drafting anticipated objections, and managing revision decisions.

This guide details how to assemble a comprehensive, reproducible replication package for academic manuscripts, especially in fields relying on complex institutional data (e.g., court records, contracts). It mandates creating a one-command master script, documenting the entire data pipeline from raw sources to final exhibits, and providing detailed protocols for hand-coded variables and data provenance. The goal is to ensure that any researcher can perfectly reproduce every finding.

A comprehensive guide detailing the mandatory replication package requirements for submitting empirical papers to the Journal of Urban Economics (JUE). It covers not only standard data and code deposits but also specialized techniques for handling complex geospatial data, geocoding pipelines, and proprietary/confidential restricted data paths to ensure maximum reproducibility.

A comprehensive guide for authors preparing their accepted KDD paper for ACM proceedings. It covers the entire production pipeline, including page budgeting (9 content pages, 3 pages for references/appendix), managing e-rights and TAPS uploads, de-anonymization steps, and ensuring proper Digital Library metadata for final publication.

This guide provides best practices for ensuring the scientific rigor and reproducibility of data mining and machine learning research published at top-tier conferences like KDD. It details a three-tier honesty model (Rerunnable, Rebuildable, Attested) and provides comprehensive checklists for disclosing pipeline stages, handling stochasticity, and documenting configurations to build reader trust.

A comprehensive project management guide for planning submissions to KDD, accounting for its unique structure of two annual submission cycles. It covers milestone tracking, cycle-choice decision matrices, managing the Resubmit loop, and planning evidence lead times from abstract to camera-ready status. Essential for complex, multi-stage data science research.

Prepares datasets, code, glossed corpora, and sound files for a Language (LSA) manuscript. Covers reproducible quantitative pipelines, Leipzig-glossed fieldwork examples, phonetic scripts, community consent and anonymization ethics, and data-availability statements.

Comprehensive guidelines for auditing and reproducing high-stakes empirical financial research. This resource details the rigorous standards required for top-tier finance journals, covering sample construction, identifying variables, advanced econometric modeling (e.g., panel data, factor pricing), handling international cross-market data, and establishing a fully reproducible data pipeline. Essential for ensuring the robustness and credibility of finance findings.

A comprehensive guide for assembling a reproducible data and code package for publishing economic manuscripts, specialized for World Bank Economic Review (WBER) standards. It details best practices for data availability statements, handling restricted microdata, pinning software versions, and ensuring full transparency throughout the analysis pipeline.

gget is a versatile CLI and Python package designed for rapid, reproducible bioinformatics searches across major genomic reference databases (e.g., Ensembl, UniProt). It allows users to quickly query gene metadata, perform initial BLAST/BLAT style alignments, and explore associated pathways, cancer, or disease modules. It is ideal for initial data exploration and generating reproducible evidence logs before building complex, full-scale local pipelines.